[We derive a measure of aggregate systemic risk, designated CATFIN, that complements bank-specific systemic risk measures by forecasting macroeconomic downturns six months into the future using out-of-sample tests conducted with U.S., European, and Asian bank data. Consistent with bank "specialness," the CATFIN of both large and small banks forecasts macroeconomic declines, whereas a similarly defined measure for both nonfinancial firms and simulated "fake banks" has no marginal predictive ability. High levels of systemic risk in the banking sector impact the macroeconomy through aggregate lending activity. A conditional asset pricing model shows that CATFIN is priced for financial and nonfinancial firms.]

Drawing upon more than 12 million observations over the period from 1996 to 2020, we find that allowing for nonlinearities significantly increases the out-of-sample performance of option and stock characteristics in predicting future option returns. The nonlinear machine learning models generate statistically and economically sizable profits in the long-short portfolios of equity options even after accounting for transaction costs. Although option-based characteristics are the most important standalone predictors, stock-based measures offer substantial incremental predictive power when considered alongside option-based characteristics. Finally, we provide compelling evidence that option return predictability is driven by informational frictions and option mispricing.

We find that the stock market underreacts to stock-level liquidity shocks: liquidity shocks are not only positively associated with contemporaneous returns, but they also predict future return continuations for up to six months. Long-short portfolios sorted on liquidity shocks generate significant returns of 0.70% to 1.20% per month that are robust across alternative shock measures and after controlling for risk factors and stock characteristics. Furthermore, we show that investor inattention and illiquidity contribute to the underreaction: while both are significant in explaining short-term return predictability of liquidity shocks, the inattention-based mechanism is more powerful for the longer-term return predictability.

We derive a measure of aggregate systemic risk, designated CATFIN, that complements bank-specific systemic risk measures by forecasting macroeconomic downturns six months into the future using out-of-sample tests conducted with U.S., European, and Asian bank data. Consistent with bank “specialness,” the CATFIN of both large and small banks forecasts macroeconomic declines, whereas a similarly defined measure for both nonfinancial firms and simulated “fake banks” has no marginal predictive ability. High levels of systemic risk in the banking sector impact the macroeconomy through aggregate lending activity. A conditional asset pricing model shows that CATFIN is priced for financial and nonfinancial firms.

We propose a statistical model of heterogeneous beliefs wherein investors are represented as different machine learning model specifications. Investors form return forecasts from their individual models using common data inputs. We measure disagreement as forecast dispersion across investor-models (MFD). Our measure aligns with analyst forecast disagreement but more powerfully predicts returns. We document a large and robust association between belief disagreement and future returns. A decile spread portfolio that sells stocks with high disagreement and buys stocks with low disagreement earns a value-weighted return of 13% per year. Further analyses suggest MFD-alpha is mispricing induced by short-sale costs and limits-to-arbitrage.

We find that the stock market underreacts to stock-level liquidity shocks: liquidity shocks are not only positively associated with contemporaneous returns, but they also predict future return continuations for up to six months. Long-short portfolios sorted on liquidity shocks generate significant returns of 0.70% to 1.20% per month that are robust across alternative shock measures and after controlling for risk factors and stock characteristics. Furthermore, we show that investor inattention and illiquidity contribute to the underreaction: while both are significant in explaining short-term return predictability of liquidity shocks, the inattention-based mechanism is more powerful for the longer-term return predictability.